基于自然驾驶数据的危险事件识别方法

利用阈值法从自然驾驶数据中识别可能的危险事件,再采用随机森林模型和支持向量机模型深度筛选,克服了阈值法误报率过高的缺陷。基于上海自然驾驶数据,建立提取危险事件的阈值标准,从原始数据中识别出3 623起可能的危险事件;利用随机森林模型筛选出重要特征作为输入变量,训练机器学习模型,对测试集进行预测。结果表明,起到关键作用的变量有:纵向加速度的最小值和均值、与前车距离的最小值以及车速的标准差。相比随机森林模型,支持向量机模型预测效果更优,在控制漏报率的同时,可过滤85.9%的无效事件。

Detection of Safety-critical Events Based on Naturalistic Driving Data

Possible safety-critical events (SCEs) were identified from the naturalistic driving data using a threshold method. Random forests (RF) and support vector machine (SVM) models were employed to further screen the possible events, overcoming the defect of a high false positive rate while applying threshold methods solely. A set of threshold criteria was established and 3 623 possible SCEs were extracted from the naturalistic driving data in Shanghai. The RF method was adopted to select the important features as input variables. The RF and SVM models were trained and tested respectively on the same dataset. The results indicate that:the mean and minimum value of longitudinal acceleration, the minimum value of the distance from the leading vehicle and the standard deviation of the speed of the subject vehicle can effectively determine whether the possible events are valid or not.Compared with RF, SVM performs better in prediction, that is, filtering 85.9% invalid events and controlling false negative rate simultaneously.